AbstractThe ever-growing influence of technology in society and business creates major vulnerabilities that can be exploited through cyberattacks. Cybersecurity therefore is of fundamental importance to defend against malicious actors who regularly target ubiquitous technologies like Android OS and web applications. In theory, defensive solutions using artificial intelligence, machine learning and neural networks can offer a significant degree of automation. However, false positive rates of these systems remain a problem, whilst there are issues translating state-of-the-art lab research into the real-world, not least the difficulty in evaluation with a scarcity of representative data that is often proprietary and highly-sensitive. To help solve these challenges, this thesis presents three multi-view convolutional neural networks to advance the fields of Android malware detection and web application security.
Thesis embargoed until 31 July 2023.
|Date of Award||Jul 2022|
|Sponsors||Northern Ireland Department for the Economy|
|Supervisor||Jesus Martinez-del-Rincon (Supervisor) & Paul Miller (Supervisor)|
- application security
- malware detection
- machine learning
- deep learning
- convolutional neural networks
- multi-view learning